Bibliographic record
Abstract
BACKGROUND: Drug-resistant strains of tuberculosis (TB) pose a serious threat to TB prevention and control efforts. The Canadian Tuberculosis Laboratory Surveillance System (CTBLSS) was created in 1998 to monitor emerging trends and patterns in TB drug resistance in Canada. OBJECTIVE: To present a descriptive overview of TB drug resistance data collected through the CTBLSS for the years 2006 to 2016 in Canada, with a focus on 2016. METHODS: The CTBLSS is an isolate-based surveillance system designed to collect data on TB drug resistance across Canada. Each year, data are collected and analyzed by the Public Health Agency of Canada (PHAC) and then validated by the submitting laboratory. RESULTS: In 2016, anti-tuberculosis drug susceptibility test results were reported for 1,452 isolates. The proportion of TB drug-resistant strains remained relatively stable with 108 (7.4%) of the isolates classified as monoresistant, five (0.3%) isolates as polyresistant and 17 (1.2%) as multidrug-resistant TB (MDR-TB) strains. In 2016, there were no extensively drug-resistant TB (XDR-TB) isolates identified. Males accounted for 792 (54.5%) of all reported isolates and 64 (49.2%) of the resistant strains and females accounted for 11 (64.7%) of the MDR-TB strains. Between 2006 and 2016, individuals between 15 and 44 years of age comprised 47.4% of all reported isolates, 54.0% of isolates showing any resistance and 72.3% of MDR-TB strains. CONCLUSION: TB drug resistance levels have been relatively low and stable over the past 11 years and have remained below the global average since national surveillance began. However, with growing worldwide concern about drug resistance and the emergence of XDR-TB, the CTBLSS will remain vital to the monitoring of TB drug resistance in Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".